MUSE (Motivation-Understanding-Schema for Effectiveness)

The chatbot ALIX’s job is to encourage students. As part of the SMARTA project, we’re looking to see if students come to the chat feeling motivated or not. We’re also trying to find out if chatting with ALIX changes their motivation levels. For example, a student might start the chat in a bad mood and not feel better after talking to ALIX. We call this a ‘Stagnation Spiral.’ Ideally, we want ALIX to help students who are feeling down become more motivated, which we call a ‘Motivation Reboot.’

Currently, we can measure the mood/sentiment of each chat and how it changes (!). We don’t know the users’ names, but we can analyze and record the chat’s sentiment. This lets us track how often something like a ‘Euphoria Decline’ occurs. Right now, the percentages in our matrix are just rough estimates. We’ll publish exact numbers in spring 2024.

Machine AI vs. Human Algorithms

In 1956, at the first AI conference, there was a consensus that machines could simulate intelligence. The debate was whether this would be best achieved through machine learning algorithms or human-written code. Today, many believe that AI will revolutionize university software. However, some argue that human algorithms, which have been effective and legally compliant for years, are still essential.

We believe that human algorithms are crucial for legally significant decisions, such as calculating final grades on a diploma supplement or determining a faculty’s budget. Algorithmic Intelligence, which is code written by humans, remains vital for universities. Human algorithms follow specific rules, are precise, and lack creativity. They don’t alter themselves. When a decision is made by an algorithm, it’s possible to trace back the reasoning, ensuring legal compliance. Plus, you don’t need tons of examples to train a human algorithm.

Consider a new university using generative AI to assign final grades. They would need many examples to train the model. Then, they might end up giving different grades to similar students on different days without understanding why. That would be disastrous.

Therefore, SMARTA focuses on using AI not for decision-making tasks but for areas where creativity is key, especially in generating text. We use various language models like GPT. Instead of the standard interface, we strictly utilize the API. This approach allows us to seamlessly integrate AI into existing systems like a Campus Management System and control both input and output.